For over sixty years, astronomers and astrophysicists have been engaged in the Search for Extraterrestrial Intelligence (SETI). This consists of listening to other star systems for signs of technological activity (or “technosignatures), such as radio transmissions. This first attempt was in 1960, known as Project Ozma, where famed SETI researcher Dr. Frank Drake (father of the Drake Equation) and his colleagues used the radio telescope at the Green Bank Observatory in West Virginia to conduct a radio survey of Tau Ceti and Epsilon Eridani.
Since then, the vast majority of SETI surveys have similarly looked for narrowband radio signals since they are very good at propagating through interstellar space. However, the biggest challenge has always been how to filter out radio transmissions on Earth – aka. radio frequency interference (RFI). In a recent study, an international team led by the Dunlap Institute for Astronomy and Astrophysics (DIAA) applied a new deep-learning algorithm to data collected by the Green Bank Telescope (GBT), which revealed eight promising signals that will be of interest to SETI initiatives like Breakthrough Listen.
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